Overview

Dataset statistics

Number of variables26
Number of observations1000
Missing cells1131
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory1.6 KiB

Variable types

Numeric8
Text8
Categorical7
Boolean3

Alerts

ampsclass has constant value ""Constant
ampsid is highly overall correlated with ampsstatus and 8 other fieldsHigh correlation
ampsstatus is highly overall correlated with ampsidHigh correlation
borough is highly overall correlated with ampsid and 1 other fieldsHigh correlation
communityboard is highly overall correlated with councildistrict and 4 other fieldsHigh correlation
councildistrict is highly overall correlated with communityboard and 3 other fieldsHigh correlation
df_activated is highly overall correlated with ampsidHigh correlation
filter_installed is highly overall correlated with ampsidHigh correlation
fountaincount is highly overall correlated with ampsid and 1 other fieldsHigh correlation
fountaintype is highly overall correlated with ampsid and 2 other fieldsHigh correlation
outdoor is highly overall correlated with ampsid and 1 other fieldsHigh correlation
painted is highly overall correlated with ampsid and 2 other fieldsHigh correlation
parkdistrict is highly overall correlated with ampsid and 6 other fieldsHigh correlation
precinct is highly overall correlated with communityboard and 3 other fieldsHigh correlation
y is highly overall correlated with communityboard and 3 other fieldsHigh correlation
df_activated is highly imbalanced (66.3%)Imbalance
fountaincount is highly imbalanced (62.5%)Imbalance
filter_installed is highly imbalanced (54.8%)Imbalance
outdoor is highly imbalanced (52.2%)Imbalance
borough is highly imbalanced (91.1%)Imbalance
ampsstatus is highly imbalanced (98.8%)Imbalance
system has 11 (1.1%) missing valuesMissing
fountaincount has 11 (1.1%) missing valuesMissing
painted has 235 (23.5%) missing valuesMissing
position has 85 (8.5%) missing valuesMissing
gispropnum has 11 (1.1%) missing valuesMissing
propertyname has 12 (1.2%) missing valuesMissing
omppropid has 11 (1.1%) missing valuesMissing
subpropertyname has 658 (65.8%) missing valuesMissing
ampsid has 11 (1.1%) missing valuesMissing
ampsstatus has 11 (1.1%) missing valuesMissing
ampsparentid has 11 (1.1%) missing valuesMissing
ampsname has 11 (1.1%) missing valuesMissing
communityboard has 11 (1.1%) missing valuesMissing
councildistrict has 11 (1.1%) missing valuesMissing
precinct has 11 (1.1%) missing valuesMissing
zipcode has 11 (1.1%) missing valuesMissing
zipcode is highly skewed (γ1 = -21.93573206)Skewed
0 is uniformly distributedUniform
0 has unique valuesUnique

Reproduction

Analysis started2023-12-09 04:40:11.177316
Analysis finished2023-12-09 04:47:42.045886
Duration7 minutes and 30.87 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:42.151128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2023-12-09T04:47:42.313146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

system
Text

MISSING 

Distinct989
Distinct (%)100.0%
Missing11
Missing (%)1.1%
Memory size66.3 KiB
2023-12-09T04:47:42.584152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.114257
Min length10

Characters and Unicode

Total characters10992
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique989 ?
Unique (%)100.0%

Sample

1st rowB001-DF0207
2nd rowB002-DF0731
3rd rowB002-DF0732
4th rowB007-DF0754
5th rowB007-DF0756
ValueCountFrequency (%)
b073-df1044 1
 
0.1%
b126-df0004 1
 
0.1%
b060-df0752 1
 
0.1%
b223da-df0724 1
 
0.1%
b068-df0351 1
 
0.1%
b054-df0491 1
 
0.1%
b060-df0748 1
 
0.1%
b151-df0507 1
 
0.1%
b010-df0984 1
 
0.1%
b245-df0474 1
 
0.1%
Other values (979) 979
99.0%
2023-12-09T04:47:42.985905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1777
16.2%
D 1006
9.2%
B 998
9.1%
F 992
9.0%
- 989
9.0%
1 883
8.0%
2 802
7.3%
3 561
 
5.1%
6 548
 
5.0%
5 530
 
4.8%
Other values (19) 1906
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6920
63.0%
Uppercase Letter 3083
28.0%
Dash Punctuation 989
 
9.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1006
32.6%
B 998
32.4%
F 992
32.2%
A 37
 
1.2%
C 11
 
0.4%
J 7
 
0.2%
W 6
 
0.2%
P 5
 
0.2%
E 4
 
0.1%
G 4
 
0.1%
Other values (8) 13
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 1777
25.7%
1 883
12.8%
2 802
11.6%
3 561
 
8.1%
6 548
 
7.9%
5 530
 
7.7%
7 487
 
7.0%
8 476
 
6.9%
4 436
 
6.3%
9 420
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 989
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7909
72.0%
Latin 3083
 
28.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1006
32.6%
B 998
32.4%
F 992
32.2%
A 37
 
1.2%
C 11
 
0.4%
J 7
 
0.2%
W 6
 
0.2%
P 5
 
0.2%
E 4
 
0.1%
G 4
 
0.1%
Other values (8) 13
 
0.4%
Common
ValueCountFrequency (%)
0 1777
22.5%
- 989
12.5%
1 883
11.2%
2 802
10.1%
3 561
 
7.1%
6 548
 
6.9%
5 530
 
6.7%
7 487
 
6.2%
8 476
 
6.0%
4 436
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1777
16.2%
D 1006
9.2%
B 998
9.1%
F 992
9.0%
- 989
9.0%
1 883
8.0%
2 802
7.3%
3 561
 
5.1%
6 548
 
5.0%
5 530
 
4.8%
Other values (19) 1906
17.3%

df_activated
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
Activated
885 
Broken
 
68
UnderConstruction
 
27
NotYetActivated
 
20

Length

Max length17
Median length9
Mean length9.132
Min length6

Characters and Unicode

Total characters9132
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActivated
2nd rowBroken
3rd rowBroken
4th rowUnderConstruction
5th rowUnderConstruction

Common Values

ValueCountFrequency (%)
Activated 885
88.5%
Broken 68
 
6.8%
UnderConstruction 27
 
2.7%
NotYetActivated 20
 
2.0%

Length

2023-12-09T04:47:43.134182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T04:47:43.254227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
activated 885
88.5%
broken 68
 
6.8%
underconstruction 27
 
2.7%
notyetactivated 20
 
2.0%

Most occurring characters

ValueCountFrequency (%)
t 1904
20.8%
e 1020
11.2%
i 932
10.2%
d 932
10.2%
c 932
10.2%
A 905
9.9%
v 905
9.9%
a 905
9.9%
n 149
 
1.6%
o 142
 
1.6%
Other values (9) 406
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8065
88.3%
Uppercase Letter 1067
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1904
23.6%
e 1020
12.6%
i 932
11.6%
d 932
11.6%
c 932
11.6%
v 905
11.2%
a 905
11.2%
n 149
 
1.8%
o 142
 
1.8%
r 122
 
1.5%
Other values (3) 122
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 905
84.8%
B 68
 
6.4%
U 27
 
2.5%
C 27
 
2.5%
N 20
 
1.9%
Y 20
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1904
20.8%
e 1020
11.2%
i 932
10.2%
d 932
10.2%
c 932
10.2%
A 905
9.9%
v 905
9.9%
a 905
9.9%
n 149
 
1.6%
o 142
 
1.6%
Other values (9) 406
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1904
20.8%
e 1020
11.2%
i 932
10.2%
d 932
10.2%
c 932
10.2%
A 905
9.9%
v 905
9.9%
a 905
9.9%
n 149
 
1.6%
o 142
 
1.6%
Other values (9) 406
 
4.4%

fountaintype
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size60.5 KiB
A
266 
C
202 
E
171 
D
74 
Indoor Drinking Fountain
63 
Other values (21)
224 

Length

Max length34
Median length1
Mean length4.857
Min length1

Characters and Unicode

Total characters4857
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowA
2nd rowE
3rd rowE
4th rowCS Metal
5th rowA

Common Values

ValueCountFrequency (%)
A 266
26.6%
C 202
20.2%
E 171
17.1%
D 74
 
7.4%
Indoor Drinking Fountain 63
 
6.3%
Bottle Filler High Low 28
 
2.8%
E High Low 27
 
2.7%
F High Low 25
 
2.5%
B 21
 
2.1%
CS Concrete 20
 
2.0%
Other values (16) 103
 
10.3%

Length

2023-12-09T04:47:43.386939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 266
18.0%
e 214
14.5%
c 202
13.7%
fountain 84
 
5.7%
high 80
 
5.4%
low 80
 
5.4%
d 74
 
5.0%
drinking 68
 
4.6%
indoor 63
 
4.3%
bottle 47
 
3.2%
Other values (22) 298
20.2%

Most occurring characters

ValueCountFrequency (%)
476
 
9.8%
n 444
 
9.1%
o 437
 
9.0%
i 385
 
7.9%
A 266
 
5.5%
C 263
 
5.4%
r 238
 
4.9%
t 233
 
4.8%
E 214
 
4.4%
e 186
 
3.8%
Other values (25) 1715
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2890
59.5%
Uppercase Letter 1491
30.7%
Space Separator 476
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 444
15.4%
o 437
15.1%
i 385
13.3%
r 238
8.2%
t 233
8.1%
e 186
6.4%
g 167
 
5.8%
l 164
 
5.7%
a 125
 
4.3%
d 99
 
3.4%
Other values (9) 412
14.3%
Uppercase Letter
ValueCountFrequency (%)
A 266
17.8%
C 263
17.6%
E 214
14.4%
F 179
12.0%
D 159
10.7%
H 84
 
5.6%
L 80
 
5.4%
B 69
 
4.6%
S 64
 
4.3%
I 63
 
4.2%
Other values (5) 50
 
3.4%
Space Separator
ValueCountFrequency (%)
476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4381
90.2%
Common 476
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 444
 
10.1%
o 437
 
10.0%
i 385
 
8.8%
A 266
 
6.1%
C 263
 
6.0%
r 238
 
5.4%
t 233
 
5.3%
E 214
 
4.9%
e 186
 
4.2%
F 179
 
4.1%
Other values (24) 1536
35.1%
Common
ValueCountFrequency (%)
476
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
476
 
9.8%
n 444
 
9.1%
o 437
 
9.0%
i 385
 
7.9%
A 266
 
5.5%
C 263
 
5.4%
r 238
 
4.9%
t 233
 
4.8%
E 214
 
4.4%
e 186
 
3.8%
Other values (25) 1715
35.3%

fountaincount
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.3%
Missing11
Missing (%)1.1%
Memory size56.6 KiB
1
876 
2
91 
3
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters989
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 876
87.6%
2 91
 
9.1%
3 22
 
2.2%
(Missing) 11
 
1.1%

Length

2023-12-09T04:47:43.514294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T04:47:43.625795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 876
88.6%
2 91
 
9.2%
3 22
 
2.2%

Most occurring characters

ValueCountFrequency (%)
1 876
88.6%
2 91
 
9.2%
3 22
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 989
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 876
88.6%
2 91
 
9.2%
3 22
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 876
88.6%
2 91
 
9.2%
3 22
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 876
88.6%
2 91
 
9.2%
3 22
 
2.2%

painted
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing235
Missing (%)23.5%
Memory size2.1 KiB
False
433 
True
332 
(Missing)
235 
ValueCountFrequency (%)
False 433
43.3%
True 332
33.2%
(Missing) 235
23.5%
2023-12-09T04:47:43.737419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

filter_installed
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing9
Missing (%)0.9%
Memory size2.1 KiB
False
897 
True
94 
(Missing)
 
9
ValueCountFrequency (%)
False 897
89.7%
True 94
 
9.4%
(Missing) 9
 
0.9%
2023-12-09T04:47:43.843415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

position
Text

MISSING 

Distinct57
Distinct (%)6.2%
Missing85
Missing (%)8.5%
Memory size70.2 KiB
2023-12-09T04:47:43.997481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length74
Median length51
Mean length18.477596
Min length6

Characters and Unicode

Total characters16907
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.9%

Sample

1st rowUnder Tree, In Playground
2nd rowOut in Open
3rd rowUnder Tree, Out in Open
4th rowIn Shade
5th rowOut in Open, In Playground
ValueCountFrequency (%)
in 770
25.1%
out 400
13.0%
open 400
13.0%
playground 373
12.1%
near 205
 
6.7%
ballfield 205
 
6.7%
under 181
 
5.9%
tree 181
 
5.9%
indoor 102
 
3.3%
outside 78
 
2.5%
Other values (8) 177
 
5.8%
2023-12-09T04:47:44.329929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2157
12.8%
n 1834
 
10.8%
e 1514
 
9.0%
r 1042
 
6.2%
d 1022
 
6.0%
l 996
 
5.9%
u 933
 
5.5%
O 882
 
5.2%
a 862
 
5.1%
i 691
 
4.1%
Other values (21) 4974
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11653
68.9%
Uppercase Letter 2668
 
15.8%
Space Separator 2157
 
12.8%
Other Punctuation 429
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1834
15.7%
e 1514
13.0%
r 1042
8.9%
d 1022
8.8%
l 996
8.5%
u 933
8.0%
a 862
7.4%
i 691
 
5.9%
o 589
 
5.1%
t 568
 
4.9%
Other values (8) 1602
13.7%
Uppercase Letter
ValueCountFrequency (%)
O 882
33.1%
I 472
17.7%
P 377
14.1%
B 209
 
7.8%
N 205
 
7.7%
T 181
 
6.8%
U 181
 
6.8%
J 78
 
2.9%
S 75
 
2.8%
A 4
 
0.1%
Space Separator
ValueCountFrequency (%)
2157
100.0%
Other Punctuation
ValueCountFrequency (%)
, 429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14321
84.7%
Common 2586
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1834
12.8%
e 1514
 
10.6%
r 1042
 
7.3%
d 1022
 
7.1%
l 996
 
7.0%
u 933
 
6.5%
O 882
 
6.2%
a 862
 
6.0%
i 691
 
4.8%
o 589
 
4.1%
Other values (19) 3956
27.6%
Common
ValueCountFrequency (%)
2157
83.4%
, 429
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16907
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2157
12.8%
n 1834
 
10.8%
e 1514
 
9.0%
r 1042
 
6.2%
d 1022
 
6.0%
l 996
 
5.9%
u 933
 
5.5%
O 882
 
5.2%
a 862
 
5.1%
i 691
 
4.1%
Other values (21) 4974
29.4%

outdoor
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
897 
False
103 
ValueCountFrequency (%)
True 897
89.7%
False 103
 
10.3%
2023-12-09T04:47:44.469739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

gispropnum
Text

MISSING 

Distinct245
Distinct (%)24.8%
Missing11
Missing (%)1.1%
Memory size59.5 KiB
2023-12-09T04:47:44.846730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1183013
Min length4

Characters and Unicode

Total characters4073
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)6.9%

Sample

1st rowB001
2nd rowB002
3rd rowB002
4th rowB007
5th rowB007
ValueCountFrequency (%)
b073 74
 
7.5%
b169 52
 
5.3%
b057 28
 
2.8%
b068 25
 
2.5%
b058 25
 
2.5%
b028 24
 
2.4%
b251 23
 
2.3%
b126 22
 
2.2%
b245 17
 
1.7%
b018 14
 
1.4%
Other values (235) 685
69.3%
2023-12-09T04:47:45.400730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 992
24.4%
0 558
13.7%
2 519
12.7%
1 452
11.1%
3 264
 
6.5%
6 257
 
6.3%
5 241
 
5.9%
7 199
 
4.9%
8 184
 
4.5%
4 147
 
3.6%
Other values (19) 260
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2967
72.8%
Uppercase Letter 1106
 
27.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 992
89.7%
A 37
 
3.3%
D 17
 
1.5%
C 11
 
1.0%
J 7
 
0.6%
Q 7
 
0.6%
W 6
 
0.5%
P 5
 
0.5%
G 4
 
0.4%
E 4
 
0.4%
Other values (9) 16
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 558
18.8%
2 519
17.5%
1 452
15.2%
3 264
8.9%
6 257
8.7%
5 241
8.1%
7 199
 
6.7%
8 184
 
6.2%
4 147
 
5.0%
9 146
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2967
72.8%
Latin 1106
 
27.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 992
89.7%
A 37
 
3.3%
D 17
 
1.5%
C 11
 
1.0%
J 7
 
0.6%
Q 7
 
0.6%
W 6
 
0.5%
P 5
 
0.5%
G 4
 
0.4%
E 4
 
0.4%
Other values (9) 16
 
1.4%
Common
ValueCountFrequency (%)
0 558
18.8%
2 519
17.5%
1 452
15.2%
3 264
8.9%
6 257
8.7%
5 241
8.1%
7 199
 
6.7%
8 184
 
6.2%
4 147
 
5.0%
9 146
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 992
24.4%
0 558
13.7%
2 519
12.7%
1 452
11.1%
3 264
 
6.5%
6 257
 
6.3%
5 241
 
5.9%
7 199
 
4.9%
8 184
 
4.5%
4 147
 
3.6%
Other values (19) 260
 
6.4%

propertyname
Text

MISSING 

Distinct239
Distinct (%)24.2%
Missing12
Missing (%)1.2%
Memory size73.5 KiB
2023-12-09T04:47:45.753595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length39
Median length31
Mean length18.659919
Min length4

Characters and Unicode

Total characters18436
Distinct characters59
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)6.4%

Sample

1st rowAmerican Playground
2nd rowAmersfort Park
3rd rowAmersfort Park
4th rowBensonhurst Park
5th rowBensonhurst Park
ValueCountFrequency (%)
park 490
 
18.1%
playground 351
 
13.0%
beach 103
 
3.8%
prospect 78
 
2.9%
66
 
2.4%
island 58
 
2.1%
coney 58
 
2.1%
boardwalk 52
 
1.9%
marine 28
 
1.0%
recreation 26
 
1.0%
Other values (334) 1397
51.6%
2023-12-09T04:47:46.268663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1948
 
10.6%
r 1786
 
9.7%
1719
 
9.3%
e 1201
 
6.5%
n 1168
 
6.3%
o 1151
 
6.2%
P 1030
 
5.6%
l 854
 
4.6%
d 772
 
4.2%
k 686
 
3.7%
Other values (49) 6121
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13839
75.1%
Uppercase Letter 2696
 
14.6%
Space Separator 1719
 
9.3%
Other Punctuation 174
 
0.9%
Dash Punctuation 4
 
< 0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1948
14.1%
r 1786
12.9%
e 1201
 
8.7%
n 1168
 
8.4%
o 1151
 
8.3%
l 854
 
6.2%
d 772
 
5.6%
k 686
 
5.0%
y 564
 
4.1%
u 535
 
3.9%
Other values (15) 3174
22.9%
Uppercase Letter
ValueCountFrequency (%)
P 1030
38.2%
B 307
 
11.4%
C 175
 
6.5%
M 161
 
6.0%
S 150
 
5.6%
H 120
 
4.5%
G 105
 
3.9%
R 80
 
3.0%
J 73
 
2.7%
D 69
 
2.6%
Other values (14) 426
15.8%
Other Punctuation
ValueCountFrequency (%)
. 66
37.9%
& 52
29.9%
' 35
20.1%
/ 21
 
12.1%
Decimal Number
ValueCountFrequency (%)
3 1
25.0%
2 1
25.0%
7 1
25.0%
9 1
25.0%
Space Separator
ValueCountFrequency (%)
1719
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16535
89.7%
Common 1901
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1948
 
11.8%
r 1786
 
10.8%
e 1201
 
7.3%
n 1168
 
7.1%
o 1151
 
7.0%
P 1030
 
6.2%
l 854
 
5.2%
d 772
 
4.7%
k 686
 
4.1%
y 564
 
3.4%
Other values (39) 5375
32.5%
Common
ValueCountFrequency (%)
1719
90.4%
. 66
 
3.5%
& 52
 
2.7%
' 35
 
1.8%
/ 21
 
1.1%
- 4
 
0.2%
3 1
 
0.1%
2 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1948
 
10.6%
r 1786
 
9.7%
1719
 
9.3%
e 1201
 
6.5%
n 1168
 
6.3%
o 1151
 
6.2%
P 1030
 
5.6%
l 854
 
4.6%
d 772
 
4.2%
k 686
 
3.7%
Other values (49) 6121
33.2%

omppropid
Text

MISSING 

Distinct352
Distinct (%)35.6%
Missing11
Missing (%)1.1%
Memory size61.0 KiB
2023-12-09T04:47:46.694514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.710819
Min length4

Characters and Unicode

Total characters5648
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)11.5%

Sample

1st rowB001
2nd rowB002
3rd rowB002
4th rowB007
5th rowB007-01
ValueCountFrequency (%)
b245 16
 
1.6%
b251-zn01 16
 
1.6%
b042 12
 
1.2%
b270 11
 
1.1%
b073-zn15 11
 
1.1%
b073-zn28 11
 
1.1%
b073-zn27 11
 
1.1%
b073 11
 
1.1%
b126 11
 
1.1%
b058 10
 
1.0%
Other values (342) 869
87.9%
2023-12-09T04:47:47.247529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 1020
18.1%
0 839
14.9%
2 602
10.7%
1 597
10.6%
- 342
 
6.1%
3 337
 
6.0%
5 273
 
4.8%
Z 250
 
4.4%
N 250
 
4.4%
6 250
 
4.4%
Other values (20) 888
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3651
64.6%
Uppercase Letter 1655
29.3%
Dash Punctuation 342
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1020
61.6%
Z 250
 
15.1%
N 250
 
15.1%
A 56
 
3.4%
D 17
 
1.0%
C 15
 
0.9%
Q 7
 
0.4%
J 7
 
0.4%
W 6
 
0.4%
P 5
 
0.3%
Other values (9) 22
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 839
23.0%
2 602
16.5%
1 597
16.4%
3 337
9.2%
5 273
 
7.5%
6 250
 
6.8%
7 245
 
6.7%
4 183
 
5.0%
8 176
 
4.8%
9 149
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3993
70.7%
Latin 1655
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1020
61.6%
Z 250
 
15.1%
N 250
 
15.1%
A 56
 
3.4%
D 17
 
1.0%
C 15
 
0.9%
Q 7
 
0.4%
J 7
 
0.4%
W 6
 
0.4%
P 5
 
0.3%
Other values (9) 22
 
1.3%
Common
ValueCountFrequency (%)
0 839
21.0%
2 602
15.1%
1 597
15.0%
- 342
8.6%
3 337
8.4%
5 273
 
6.8%
6 250
 
6.3%
7 245
 
6.1%
4 183
 
4.6%
8 176
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1020
18.1%
0 839
14.9%
2 602
10.7%
1 597
10.6%
- 342
 
6.1%
3 337
 
6.0%
5 273
 
4.8%
Z 250
 
4.4%
N 250
 
4.4%
6 250
 
4.4%
Other values (20) 888
15.7%

subpropertyname
Text

MISSING 

Distinct115
Distinct (%)33.6%
Missing658
Missing (%)65.8%
Memory size47.0 KiB
2023-12-09T04:47:47.553831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length50
Median length36
Mean length21.836257
Min length9

Characters and Unicode

Total characters7468
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)12.0%

Sample

1st rowBensonhurst Park Playground
2nd rowImagination Playground at Betsy Head
3rd rowImagination Playground at Betsy Head
4th rowBrower Park
5th rowMaria Hernandez Park Playground
ValueCountFrequency (%)
zone 172
 
12.8%
park 131
 
9.7%
beach 87
 
6.5%
island 53
 
3.9%
coney 50
 
3.7%
1 49
 
3.6%
playground 43
 
3.2%
plgd 33
 
2.5%
2 32
 
2.4%
long 24
 
1.8%
Other values (142) 672
49.9%
2023-12-09T04:47:47.999528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1004
 
13.4%
e 695
 
9.3%
a 657
 
8.8%
n 598
 
8.0%
o 495
 
6.6%
r 494
 
6.6%
d 285
 
3.8%
P 253
 
3.4%
l 249
 
3.3%
s 187
 
2.5%
Other values (53) 2551
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5063
67.8%
Uppercase Letter 1183
 
15.8%
Space Separator 1004
 
13.4%
Decimal Number 180
 
2.4%
Other Punctuation 38
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 695
13.7%
a 657
13.0%
n 598
11.8%
o 495
9.8%
r 494
9.8%
d 285
 
5.6%
l 249
 
4.9%
s 187
 
3.7%
k 184
 
3.6%
t 176
 
3.5%
Other values (14) 1043
20.6%
Uppercase Letter
ValueCountFrequency (%)
P 253
21.4%
Z 173
14.6%
B 105
8.9%
M 95
 
8.0%
C 93
 
7.9%
L 88
 
7.4%
I 56
 
4.7%
S 55
 
4.6%
D 39
 
3.3%
G 34
 
2.9%
Other values (14) 192
16.2%
Decimal Number
ValueCountFrequency (%)
1 56
31.1%
2 39
21.7%
3 25
13.9%
4 19
 
10.6%
5 15
 
8.3%
7 14
 
7.8%
6 8
 
4.4%
9 2
 
1.1%
8 2
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/ 14
36.8%
' 8
21.1%
, 6
15.8%
. 5
 
13.2%
& 5
 
13.2%
Space Separator
ValueCountFrequency (%)
1004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6246
83.6%
Common 1222
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 695
 
11.1%
a 657
 
10.5%
n 598
 
9.6%
o 495
 
7.9%
r 494
 
7.9%
d 285
 
4.6%
P 253
 
4.1%
l 249
 
4.0%
s 187
 
3.0%
k 184
 
2.9%
Other values (38) 2149
34.4%
Common
ValueCountFrequency (%)
1004
82.2%
1 56
 
4.6%
2 39
 
3.2%
3 25
 
2.0%
4 19
 
1.6%
5 15
 
1.2%
7 14
 
1.1%
/ 14
 
1.1%
' 8
 
0.7%
6 8
 
0.7%
Other values (5) 20
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1004
 
13.4%
e 695
 
9.3%
a 657
 
8.8%
n 598
 
8.0%
o 495
 
6.6%
r 494
 
6.6%
d 285
 
3.8%
P 253
 
3.4%
l 249
 
3.3%
s 187
 
2.5%
Other values (53) 2551
34.2%

parkdistrict
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
B-13
94 
B-18
90 
B-01
83 
B-02
78 
B-19
78 
Other values (21)
577 

Length

Max length5
Median length4
Mean length4.006
Min length4

Characters and Unicode

Total characters4006
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowB-01
2nd rowB-18
3rd rowB-18
4th rowB-11
5th rowB-11

Common Values

ValueCountFrequency (%)
B-13 94
 
9.4%
B-18 90
 
9.0%
B-01 83
 
8.3%
B-02 78
 
7.8%
B-19 78
 
7.8%
B-10 74
 
7.4%
B-03 64
 
6.4%
B-06 59
 
5.9%
B-15 56
 
5.6%
B-16 53
 
5.3%
Other values (16) 271
27.1%

Length

2023-12-09T04:47:48.140501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b-13 94
 
9.4%
b-18 90
 
9.0%
b-01 83
 
8.3%
b-02 78
 
7.8%
b-19 78
 
7.8%
b-10 74
 
7.4%
b-03 64
 
6.4%
b-06 59
 
5.9%
b-15 56
 
5.6%
b-16 53
 
5.3%
Other values (16) 271
27.1%

Most occurring characters

ValueCountFrequency (%)
- 1000
25.0%
B 981
24.5%
1 650
16.2%
0 527
13.2%
3 160
 
4.0%
8 135
 
3.4%
6 112
 
2.8%
2 106
 
2.6%
5 98
 
2.4%
9 97
 
2.4%
Other values (5) 140
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
49.9%
Uppercase Letter 1006
25.1%
Dash Punctuation 1000
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 650
32.5%
0 527
26.4%
3 160
 
8.0%
8 135
 
6.8%
6 112
 
5.6%
2 106
 
5.3%
5 98
 
4.9%
9 97
 
4.9%
4 62
 
3.1%
7 53
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
B 981
97.5%
Q 18
 
1.8%
A 6
 
0.6%
X 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3000
74.9%
Latin 1006
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1000
33.3%
1 650
21.7%
0 527
17.6%
3 160
 
5.3%
8 135
 
4.5%
6 112
 
3.7%
2 106
 
3.5%
5 98
 
3.3%
9 97
 
3.2%
4 62
 
2.1%
Latin
ValueCountFrequency (%)
B 981
97.5%
Q 18
 
1.8%
A 6
 
0.6%
X 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
25.0%
B 981
24.5%
1 650
16.2%
0 527
13.2%
3 160
 
4.0%
8 135
 
3.4%
6 112
 
2.8%
2 106
 
2.6%
5 98
 
2.4%
9 97
 
2.4%
Other values (5) 140
 
3.5%

borough
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
B
981 
Q
 
18
X
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 981
98.1%
Q 18
 
1.8%
X 1
 
0.1%

Length

2023-12-09T04:47:48.256481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T04:47:48.369348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
b 981
98.1%
q 18
 
1.8%
x 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 981
98.1%
Q 18
 
1.8%
X 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 981
98.1%
Q 18
 
1.8%
X 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 981
98.1%
Q 18
 
1.8%
X 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 981
98.1%
Q 18
 
1.8%
X 1
 
0.1%

ampsid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct989
Distinct (%)100.0%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1.0003923 × 108
Minimum1.000374 × 108
Maximum1.0004377 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:48.490372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.000374 × 108
5-th percentile1.0003747 × 108
Q11.0003778 × 108
median1.0003836 × 108
Q31.0003995 × 108
95-th percentile1.0004303 × 108
Maximum1.0004377 × 108
Range6369
Interquartile range (IQR)2170

Descriptive statistics

Standard deviation1893.7001
Coefficient of variation (CV)1.8929575 × 10-5
Kurtosis-0.06951041
Mean1.0003923 × 108
Median Absolute Deviation (MAD)786
Skewness1.1315428
Sum9.8938796 × 1010
Variance3586100
MonotonicityNot monotonic
2023-12-09T04:47:48.645607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100037725 1
 
0.1%
100037709 1
 
0.1%
100037739 1
 
0.1%
100043071 1
 
0.1%
100037425 1
 
0.1%
100042947 1
 
0.1%
100040296 1
 
0.1%
100039980 1
 
0.1%
100037493 1
 
0.1%
100042942 1
 
0.1%
Other values (979) 979
97.9%
(Missing) 11
 
1.1%
ValueCountFrequency (%)
100037403 1
0.1%
100037404 1
0.1%
100037405 1
0.1%
100037406 1
0.1%
100037407 1
0.1%
100037408 1
0.1%
100037409 1
0.1%
100037410 1
0.1%
100037411 1
0.1%
100037414 1
0.1%
ValueCountFrequency (%)
100043772 1
0.1%
100043771 1
0.1%
100043770 1
0.1%
100043769 1
0.1%
100043768 1
0.1%
100043767 1
0.1%
100043766 1
0.1%
100043765 1
0.1%
100043598 1
0.1%
100043595 1
0.1%

ampsclass
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
DRINKFTN
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRINKFTN
2nd rowDRINKFTN
3rd rowDRINKFTN
4th rowDRINKFTN
5th rowDRINKFTN

Common Values

ValueCountFrequency (%)
DRINKFTN 1000
100.0%

Length

2023-12-09T04:47:48.784011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T04:47:48.888906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
drinkftn 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
N 2000
25.0%
D 1000
12.5%
R 1000
12.5%
I 1000
12.5%
K 1000
12.5%
F 1000
12.5%
T 1000
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2000
25.0%
D 1000
12.5%
R 1000
12.5%
I 1000
12.5%
K 1000
12.5%
F 1000
12.5%
T 1000
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 8000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2000
25.0%
D 1000
12.5%
R 1000
12.5%
I 1000
12.5%
K 1000
12.5%
F 1000
12.5%
T 1000
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2000
25.0%
D 1000
12.5%
R 1000
12.5%
I 1000
12.5%
K 1000
12.5%
F 1000
12.5%
T 1000
12.5%

ampsstatus
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)1.1%
Memory size56.6 KiB
I
988 
D
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters989
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowD
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 988
98.8%
D 1
 
0.1%
(Missing) 11
 
1.1%

Length

2023-12-09T04:47:48.981701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T04:47:49.090112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
i 988
99.9%
d 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 988
99.9%
D 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 989
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 988
99.9%
D 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 989
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 988
99.9%
D 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 988
99.9%
D 1
 
0.1%

ampsparentid
Text

MISSING 

Distinct389
Distinct (%)39.3%
Missing11
Missing (%)1.1%
Memory size61.9 KiB
2023-12-09T04:47:49.410352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.6370071
Min length4

Characters and Unicode

Total characters6564
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)13.8%

Sample

1st rowB001
2nd rowB002
3rd rowB002
4th rowB007
5th rowB007-01
ValueCountFrequency (%)
b251-zn01 16
 
1.6%
b245 12
 
1.2%
b042 12
 
1.2%
b073-zn28 11
 
1.1%
b073-zn15 11
 
1.1%
b058-zn01-blg0984 10
 
1.0%
b028-zn01 9
 
0.9%
b166d 9
 
0.9%
b073-zn27 9
 
0.9%
b270-blg1185 8
 
0.8%
Other values (379) 882
89.2%
2023-12-09T04:47:49.908910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 1123
17.1%
0 908
13.8%
1 737
11.2%
2 657
10.0%
- 466
 
7.1%
3 362
 
5.5%
5 294
 
4.5%
6 270
 
4.1%
N 267
 
4.1%
Z 267
 
4.1%
Other values (21) 1213
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4105
62.5%
Uppercase Letter 1993
30.4%
Dash Punctuation 466
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1123
56.3%
N 267
 
13.4%
Z 267
 
13.4%
G 107
 
5.4%
L 103
 
5.2%
A 51
 
2.6%
D 17
 
0.9%
C 15
 
0.8%
Q 7
 
0.4%
J 7
 
0.4%
Other values (10) 29
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 908
22.1%
1 737
18.0%
2 657
16.0%
3 362
 
8.8%
5 294
 
7.2%
6 270
 
6.6%
7 259
 
6.3%
8 219
 
5.3%
4 212
 
5.2%
9 187
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4571
69.6%
Latin 1993
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1123
56.3%
N 267
 
13.4%
Z 267
 
13.4%
G 107
 
5.4%
L 103
 
5.2%
A 51
 
2.6%
D 17
 
0.9%
C 15
 
0.8%
Q 7
 
0.4%
J 7
 
0.4%
Other values (10) 29
 
1.5%
Common
ValueCountFrequency (%)
0 908
19.9%
1 737
16.1%
2 657
14.4%
- 466
10.2%
3 362
 
7.9%
5 294
 
6.4%
6 270
 
5.9%
7 259
 
5.7%
8 219
 
4.8%
4 212
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1123
17.1%
0 908
13.8%
1 737
11.2%
2 657
10.0%
- 466
 
7.1%
3 362
 
5.5%
5 294
 
4.5%
6 270
 
4.1%
N 267
 
4.1%
Z 267
 
4.1%
Other values (21) 1213
18.5%

ampsname
Text

MISSING 

Distinct619
Distinct (%)62.6%
Missing11
Missing (%)1.1%
Memory size87.6 KiB
2023-12-09T04:47:50.234671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length80
Median length60
Mean length33.217391
Min length6

Characters and Unicode

Total characters32852
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)42.1%

Sample

1st rowA-American Playground
2nd rowE-Amersfort Park
3rd rowE-Amersfort Park
4th rowCS Metal-Bensonhurst Park
5th rowA-Bensonhurst Park-Bensonhurst Park Playground
ValueCountFrequency (%)
playground 373
 
8.9%
park 324
 
7.7%
beach 185
 
4.4%
zone 157
 
3.7%
island 106
 
2.5%
high 74
 
1.8%
73
 
1.7%
indoor 63
 
1.5%
drinking 59
 
1.4%
center 49
 
1.2%
Other values (814) 2743
65.2%
2023-12-09T04:47:50.740408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3217
 
9.8%
a 2749
 
8.4%
r 2563
 
7.8%
n 2261
 
6.9%
e 2170
 
6.6%
o 2122
 
6.5%
- 1409
 
4.3%
P 1312
 
4.0%
l 1229
 
3.7%
d 1132
 
3.4%
Other values (56) 12688
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22090
67.2%
Uppercase Letter 5745
 
17.5%
Space Separator 3217
 
9.8%
Dash Punctuation 1409
 
4.3%
Other Punctuation 212
 
0.6%
Decimal Number 179
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2749
12.4%
r 2563
11.6%
n 2261
10.2%
e 2170
9.8%
o 2122
9.6%
l 1229
 
5.6%
d 1132
 
5.1%
i 1006
 
4.6%
t 997
 
4.5%
k 948
 
4.3%
Other values (15) 4913
22.2%
Uppercase Letter
ValueCountFrequency (%)
P 1312
22.8%
C 610
10.6%
B 484
 
8.4%
A 360
 
6.3%
S 297
 
5.2%
E 282
 
4.9%
D 274
 
4.8%
M 271
 
4.7%
L 250
 
4.4%
H 245
 
4.3%
Other values (15) 1360
23.7%
Decimal Number
ValueCountFrequency (%)
1 51
28.5%
2 43
24.0%
3 23
12.8%
4 18
 
10.1%
7 15
 
8.4%
5 15
 
8.4%
6 8
 
4.5%
9 3
 
1.7%
8 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 69
32.5%
& 59
27.8%
' 48
22.6%
/ 30
14.2%
, 6
 
2.8%
Space Separator
ValueCountFrequency (%)
3217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27835
84.7%
Common 5017
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2749
 
9.9%
r 2563
 
9.2%
n 2261
 
8.1%
e 2170
 
7.8%
o 2122
 
7.6%
P 1312
 
4.7%
l 1229
 
4.4%
d 1132
 
4.1%
i 1006
 
3.6%
t 997
 
3.6%
Other values (40) 10294
37.0%
Common
ValueCountFrequency (%)
3217
64.1%
- 1409
28.1%
. 69
 
1.4%
& 59
 
1.2%
1 51
 
1.0%
' 48
 
1.0%
2 43
 
0.9%
/ 30
 
0.6%
3 23
 
0.5%
4 18
 
0.4%
Other values (6) 50
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3217
 
9.8%
a 2749
 
8.4%
r 2563
 
7.8%
n 2261
 
6.9%
e 2170
 
6.6%
o 2122
 
6.5%
- 1409
 
4.3%
P 1312
 
4.0%
l 1229
 
3.7%
d 1132
 
3.4%
Other values (56) 12688
38.6%

communityboard
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.2%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean313.10313
Minimum208
Maximum403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:50.889466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum208
5-th percentile301
Q1304
median310
Q3316
95-th percentile355
Maximum403
Range195
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.490451
Coefficient of variation (CV)0.04628012
Kurtosis7.3303931
Mean313.10313
Median Absolute Deviation (MAD)6
Skewness1.763735
Sum309659
Variance209.97316
MonotonicityNot monotonic
2023-12-09T04:47:51.015879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
313 94
 
9.4%
318 93
 
9.3%
301 83
 
8.3%
355 82
 
8.2%
302 78
 
7.8%
310 74
 
7.4%
303 64
 
6.4%
306 59
 
5.9%
316 53
 
5.3%
315 52
 
5.2%
Other values (12) 257
25.7%
ValueCountFrequency (%)
208 1
 
0.1%
301 83
8.3%
302 78
7.8%
303 64
6.4%
304 25
 
2.5%
305 39
3.9%
306 59
5.9%
307 34
3.4%
308 40
4.0%
309 19
 
1.9%
ValueCountFrequency (%)
403 1
 
0.1%
356 1
 
0.1%
355 82
8.2%
318 93
9.3%
317 19
 
1.9%
316 53
5.3%
315 52
5.2%
314 34
 
3.4%
313 94
9.4%
312 26
 
2.6%

councildistrict
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)1.8%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean40.220425
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:51.142042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile33
Q136
median40
Q344
95-th percentile48
Maximum48
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8332464
Coefficient of variation (CV)0.12016895
Kurtosis0.25202655
Mean40.220425
Median Absolute Deviation (MAD)4
Skewness-0.20729091
Sum39778
Variance23.360271
MonotonicityNot monotonic
2023-12-09T04:47:51.259292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
39 116
11.6%
43 96
9.6%
33 94
9.4%
46 79
 
7.9%
47 72
 
7.2%
36 68
 
6.8%
41 65
 
6.5%
48 59
 
5.9%
35 57
 
5.7%
38 56
 
5.6%
Other values (8) 227
22.7%
ValueCountFrequency (%)
11 1
 
0.1%
21 1
 
0.1%
33 94
9.4%
34 51
5.1%
35 57
5.7%
36 68
6.8%
37 37
 
3.7%
38 56
5.6%
39 116
11.6%
40 38
 
3.8%
ValueCountFrequency (%)
48 59
5.9%
47 72
7.2%
46 79
7.9%
45 25
 
2.5%
44 25
 
2.5%
43 96
9.6%
42 49
4.9%
41 65
6.5%
40 38
 
3.8%
39 116
11.6%

precinct
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)2.5%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean73.461072
Minimum50
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:51.383882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q166
median73
Q379
95-th percentile90
Maximum115
Range65
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.6027235
Coefficient of variation (CV)0.13071853
Kurtosis-0.44148421
Mean73.461072
Median Absolute Deviation (MAD)6
Skewness0.37787469
Sum72653
Variance92.212299
MonotonicityNot monotonic
2023-12-09T04:47:51.507860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
78 102
 
10.2%
60 94
 
9.4%
68 74
 
7.4%
63 67
 
6.7%
61 53
 
5.3%
73 53
 
5.3%
90 47
 
4.7%
76 44
 
4.4%
84 40
 
4.0%
79 40
 
4.0%
Other values (15) 375
37.5%
ValueCountFrequency (%)
50 1
 
0.1%
60 94
9.4%
61 53
5.3%
62 18
 
1.8%
63 67
6.7%
66 26
 
2.6%
67 19
 
1.9%
68 74
7.4%
69 26
 
2.6%
70 34
 
3.4%
ValueCountFrequency (%)
115 1
 
0.1%
94 36
 
3.6%
90 47
4.7%
88 38
 
3.8%
84 40
 
4.0%
83 25
 
2.5%
81 24
 
2.4%
79 40
 
4.0%
78 102
10.2%
77 35
 
3.5%

zipcode
Real number (ℝ)

MISSING  SKEWED 

Distinct39
Distinct (%)3.9%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean11218.749
Minimum10471
Maximum11368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:54.197465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10471
5-th percentile11201
Q111211
median11218
Q311229
95-th percentile11236
Maximum11368
Range897
Interquartile range (IQR)18

Descriptive statistics

Standard deviation26.758627
Coefficient of variation (CV)0.0023851702
Kurtosis618.97794
Mean11218.749
Median Absolute Deviation (MAD)10
Skewness-21.935732
Sum11095343
Variance716.0241
MonotonicityNot monotonic
2023-12-09T04:47:54.338224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
11215 86
 
8.6%
11201 63
 
6.3%
11224 62
 
6.2%
11234 52
 
5.2%
11235 45
 
4.5%
11212 40
 
4.0%
11206 39
 
3.9%
11233 37
 
3.7%
11231 34
 
3.4%
11207 33
 
3.3%
Other values (29) 498
49.8%
ValueCountFrequency (%)
10471 1
 
0.1%
11201 63
6.3%
11203 20
 
2.0%
11204 16
 
1.6%
11205 23
 
2.3%
11206 39
3.9%
11207 33
3.3%
11208 20
 
2.0%
11209 24
 
2.4%
11210 8
 
0.8%
ValueCountFrequency (%)
11368 1
 
0.1%
11249 6
 
0.6%
11238 15
 
1.5%
11237 12
 
1.2%
11236 23
2.3%
11235 45
4.5%
11234 52
5.2%
11233 37
3.7%
11232 12
 
1.2%
11231 34
3.4%

x
Real number (ℝ)

Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean996064.59
Minimum972978.27
Maximum1043058.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:54.493546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum972978.27
5-th percentile979354.18
Q1988439.09
median994811.12
Q31003155
95-th percentile1014054.3
Maximum1043058.6
Range70080.367
Interquartile range (IQR)14715.937

Descriptive statistics

Standard deviation10821.05
Coefficient of variation (CV)0.010863803
Kurtosis0.052833399
Mean996064.59
Median Absolute Deviation (MAD)7184.2032
Skewness0.31397714
Sum9.9606459 × 108
Variance1.1709512 × 108
MonotonicityNot monotonic
2023-12-09T04:47:54.659831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998129.2751 10
 
1.0%
1011223.844 8
 
0.8%
991544.3145 7
 
0.7%
993829.7905 5
 
0.5%
983128.0108 5
 
0.5%
995250.8697 4
 
0.4%
983661.3397 3
 
0.3%
1002310.569 3
 
0.3%
991643.9422 2
 
0.2%
1008610.035 2
 
0.2%
Other values (941) 951
95.1%
ValueCountFrequency (%)
972978.2656 1
0.1%
972985.0688 1
0.1%
973028.862 1
0.1%
973137.0094 1
0.1%
973208.5628 1
0.1%
973338.2088 1
0.1%
973428.7113 1
0.1%
973501.4532 1
0.1%
973526.3731 1
0.1%
973578.4459 1
0.1%
ValueCountFrequency (%)
1043058.632 1
0.1%
1037323.596 1
0.1%
1032237.938 1
0.1%
1030314.753 1
0.1%
1028613.914 1
0.1%
1024390.501 1
0.1%
1022368.925 1
0.1%
1022368.195 1
0.1%
1022247.727 1
0.1%
1020337.704 1
0.1%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177062.97
Minimum147465.87
Maximum261937.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T04:47:54.832546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum147465.87
5-th percentile148714.34
Q1165160.89
median180198.81
Q3188547.01
95-th percentile199942.44
Maximum261937.33
Range114471.46
Interquartile range (IQR)23386.12

Descriptive statistics

Standard deviation15796.429
Coefficient of variation (CV)0.089213625
Kurtosis-0.026546284
Mean177062.97
Median Absolute Deviation (MAD)11239.526
Skewness-0.14852832
Sum1.7706297 × 108
Variance2.4952717 × 108
MonotonicityNot monotonic
2023-12-09T04:47:54.988829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201625.6732 10
 
1.0%
178371.2132 8
 
0.8%
182246.9728 7
 
0.7%
181688.6006 5
 
0.5%
184243.6457 5
 
0.5%
199777.3118 4
 
0.4%
184789.1669 3
 
0.3%
174967.3188 3
 
0.3%
158014.6427 2
 
0.2%
191473.1434 2
 
0.2%
Other values (941) 951
95.1%
ValueCountFrequency (%)
147465.8698 1
0.1%
147466.0732 1
0.1%
147496.164 1
0.1%
147496.5518 1
0.1%
147533.3244 1
0.1%
147543.5521 1
0.1%
147567.3102 1
0.1%
147600.5559 1
0.1%
147600.9322 1
0.1%
147691.3854 1
0.1%
ValueCountFrequency (%)
261937.3325 1
0.1%
218214.5915 1
0.1%
215389.7609 1
0.1%
215004.6829 1
0.1%
214323.47 1
0.1%
212001.1423 1
0.1%
211504.6922 1
0.1%
209737.7594 1
0.1%
209610.5339 1
0.1%
207564.4662 1
0.1%

Interactions

2023-12-09T04:46:14.568616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:14.899115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:54.470672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:12.752518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:51.570256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:30.533217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:08.792022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:48.417454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:46:20.463389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:15.044147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:59.688764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:12.933630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:51.739407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:30.717217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:09.023850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:54.256294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:46:37.335438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:26.010823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:15.561453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:23.765325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:02.599514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:41.568016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:19.957059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:11.025576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:46:45.231014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:26.662708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:20.900922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:23.984668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:02.803545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:41.792579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:20.227145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:16.906497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:46:51.064122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:26.838937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:25.808310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:24.191777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:02.991009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:41.998483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:20.482535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:22.756647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:46:56.963784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:27.040079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:31.305415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:24.419158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:03.207166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:42.234323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:20.760785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:30.350928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:47:02.838846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:27.334318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:36.363540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:24.706988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:03.487272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:42.537741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:21.103362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:36.374238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:47:21.482978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:40:40.778165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:41:54.811206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:42:38.706668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:16.355331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:43:55.454575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:44:35.386919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-09T04:45:55.528298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-12-09T04:47:55.154721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
0ampsidampsstatusboroughcommunityboardcouncildistrictdf_activatedfilter_installedfountaincountfountaintypeoutdoorpaintedparkdistrictprecinctxyzipcode
01.000-0.1040.0000.085-0.0640.0380.1410.0640.0760.1670.2440.2590.411-0.077-0.096-0.016-0.088
ampsid-0.1041.0001.0001.0000.2180.1351.0001.0001.0001.0001.0001.0001.000-0.1030.035-0.1940.052
ampsstatus0.0001.0001.0000.0000.0500.0500.0000.0300.0000.0000.0260.0000.000-0.053-0.041-0.054-0.008
borough0.0851.0000.0001.000-0.053-0.0880.0000.0000.0000.1020.0150.0000.988-0.029-0.0960.186-0.070
communityboard-0.0640.2180.050-0.0531.0000.6670.1990.1360.2020.1450.2810.5110.970-0.617-0.102-0.6670.378
councildistrict0.0380.1350.050-0.0880.6671.0000.1830.0950.1270.1460.1700.4210.764-0.904-0.167-0.9240.408
df_activated0.1411.0000.0000.0000.1990.1831.0000.2580.0450.2010.1460.0790.207-0.063-0.026-0.0660.005
filter_installed0.0641.0000.0300.0000.1360.0950.2581.0000.0390.0850.0000.0000.1320.027-0.0140.040-0.032
fountaincount0.0761.0000.0000.0000.2020.1270.0450.0391.0000.9740.0360.0620.1470.108-0.0080.066-0.037
fountaintype0.1671.0000.0000.1020.1450.1460.2010.0850.9741.0000.9390.2690.1600.0020.039-0.0450.021
outdoor0.2441.0000.0260.0150.2810.1700.1460.0000.0360.9391.0000.0000.283-0.141-0.056-0.0890.059
painted0.2591.0000.0000.0000.5110.4210.0790.0000.0620.2690.0001.0000.5100.249-0.0860.265-0.029
parkdistrict0.4111.0000.0000.9880.9700.7640.2070.1320.1470.1600.2830.5101.000-0.623-0.134-0.6000.340
precinct-0.077-0.103-0.053-0.029-0.617-0.904-0.0630.0270.1080.002-0.1410.249-0.6231.0000.1370.945-0.389
x-0.0960.035-0.041-0.096-0.102-0.167-0.026-0.014-0.0080.039-0.056-0.086-0.1340.1371.0000.053-0.080
y-0.016-0.194-0.0540.186-0.667-0.924-0.0660.0400.066-0.045-0.0890.265-0.6000.9450.0531.000-0.403
zipcode-0.0880.052-0.008-0.0700.3780.4080.005-0.032-0.0370.0210.059-0.0290.340-0.389-0.080-0.4031.000

Missing values

2023-12-09T04:47:40.796606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-09T04:47:41.335791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-09T04:47:41.678092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

0systemdf_activatedfountaintypefountaincountpaintedfilter_installedpositionoutdoorgispropnumpropertynameomppropidsubpropertynameparkdistrictboroughampsidampsclassampsstatusampsparentidampsnamecommunityboardcouncildistrictprecinctzipcodexy
01B001-DF0207ActivatedA1truefalseUnder Tree, In PlaygroundtrueB001American PlaygroundB001NaNB-01B100037676DRINKFTNDB001A-American Playground301339411222995853.631964474204780.745671972
12B002-DF0731BrokenE1truefalseOut in OpentrueB002Amersfort ParkB002NaNB-18B100039159DRINKFTNIB002E-Amersfort Park3184563112101001205.71968546168004.324802398
23B002-DF0732BrokenE1truefalseUnder Tree, Out in OpentrueB002Amersfort ParkB002NaNB-18B100039177DRINKFTNIB002E-Amersfort Park3184563112101001167.86936746168468.868820816
34B007-DF0754UnderConstructionCS Metal1falsefalseIn ShadetrueB007Bensonhurst ParkB007NaNB-11B100039198DRINKFTNIB007CS Metal-Bensonhurst Park311436211214984267.19581981157078.860104814
45B007-DF0756UnderConstructionA1truetrueOut in Open, In PlaygroundtrueB007Bensonhurst ParkB007-01Bensonhurst Park PlaygroundB-11B100039249DRINKFTNIB007-01A-Bensonhurst Park-Bensonhurst Park Playground311436211214984301.209531307156806.697591394
56B007-DF0757UnderConstructionA1truetrueNear Ballfield, Out in OpentrueB007Bensonhurst ParkB007NaNB-11B100039244DRINKFTNIB007A-Bensonhurst Park311436211214983842.562154635156796.758306816
67B007-DF0765BrokenA1truefalseOut in OpentrueB007Bensonhurst ParkB007NaNB-11B100039304DRINKFTNIB007A-Bensonhurst Park311436211214983695.758642465156090.611097052
78B007-DF0766UnderConstructionA1truetrueNear Ballfield, Out in OpentrueB007Bensonhurst ParkB007NaNB-11B100039306DRINKFTNIB007A-Bensonhurst Park311436211214983925.259823888157053.374591469
89B007-DF0767BrokenA1truefalseOut in OpentrueB007Bensonhurst ParkB007NaNB-11B100039333DRINKFTNIB007A-Bensonhurst Park311436211214983845.994234383156277.020173057
910B008-DF0018ActivatedF High Low2truefalseJust Outside PlaygroundtrueB008Betsy Head ParkB008-03Imagination Playground at Betsy HeadB-16B100037440DRINKFTNIB008-03F High Low-Betsy Head Park-Imagination Playground at Betsy Head3164173112121008748.32500705181283.89502798
0systemdf_activatedfountaintypefountaincountpaintedfilter_installedpositionoutdoorgispropnumpropertynameomppropidsubpropertynameparkdistrictboroughampsidampsclassampsstatusampsparentidampsnamecommunityboardcouncildistrictprecinctzipcodexy
990991B329-DF0820ActivatedE High Low2truefalseOut in Open, Just Outside PlaygroundtrueB329Lindower ParkB329-01Lindower Park PlaygroundB-18B100039832DRINKFTNIB329-01E High Low-Lindower Park-Lindower Park Playground3184663112341008965.24812963162403.85503222
991992B330-DF0557ActivatedD1falsefalseOut in Open, Near BallfieldtrueB330Hickman PlaygroundB330NaNB-18B100038324DRINKFTNIB330D-Hickman Playground3184663112341008446.32101789165368.022660315
992993B330-DF0558ActivatedD1falsefalseOut in Open, Near BallfieldtrueB330Hickman PlaygroundB330NaNB-18B100038327DRINKFTNIB330D-Hickman Playground3184663112341008348.13223788165362.73887822
993994B330-DF0559ActivatedD1falsefalseOut in Open, Just Outside PlaygroundtrueB330Hickman PlaygroundB330NaNB-18B100038342DRINKFTNIB330D-Hickman Playground3184663112341008204.03705364165357.569925308
994995B330-DF0901ActivatedA1NaNfalseIn PlaygroundtrueB330Hickman PlaygroundB330NaNB-18B100040299DRINKFTNIB330A-Hickman Playground3184663112341008554.24567081165264.145243391
995996B331-DF0286ActivatedC1truefalseUnder Tree, Out in Open, In Shade, In PlaygroundtrueB331Neptune PlaygroundB331NaNB-13B100037694DRINKFTNIB331C-Neptune Playground313476011224989635.091908142150017.550408557
996997B332-DF0038BrokenC1falsefalseUnder Tree, In Shade, Near BallfieldtrueB332Evergreen PlaygroundB332NaNB-04B100037442DRINKFTNIB332C-Evergreen Playground3043783112071008976.53025931189239.967042312
997998B332-DF0039BrokenC1truefalseOut in OpentrueB332Evergreen PlaygroundB332NaNB-04B100037422DRINKFTNIB332C-Evergreen Playground3043783112071008973.02140805189296.850458726
998999B334-DF0019ActivatedA1falsetrueNear BallfieldtrueB334Fermi PlaygroundB334NaNB-04B100037419DRINKFTNIB334A-Fermi Playground3043483112211004206.65528464194595.955017983
9991000B334-DF0020ActivatedC1truetrueOut in OpentrueB334Fermi PlaygroundB334NaNB-04B100037435DRINKFTNIB334C-Fermi Playground3043483112211004044.54963772194434.659408807